In [10]:
plt.imshow(image)
plt.show()
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import dlib
import cv2
import matplotlib.pyplot as plt
image = cv2.imread('/content/people1.jpg')
display (image.shape)
(1280, 1920, 3)
plt.imshow(image)
plt.show()
print(image)
[[[ 91 103 107] [102 114 118] [ 92 107 110] ... [187 192 193] [186 191 192] [185 190 191]] [[ 93 105 109] [ 97 109 113] [ 95 110 113] ... [187 192 193] [186 191 192] [185 190 191]] [[ 96 111 114] [ 96 111 114] [102 117 120] ... [186 191 192] [185 190 191] [184 189 190]] ... [[ 48 43 40] [ 46 41 38] [ 44 39 36] ... [251 248 244] [252 247 244] [251 246 243]] [[ 55 49 44] [ 53 47 42] [ 49 43 38] ... [251 248 244] [252 247 244] [252 247 244]] [[ 56 50 45] [ 54 48 43] [ 50 44 39] ... [252 249 245] [253 248 245] [253 248 245]]]
from google.colab.patches import cv2_imshow
from IPython.display import Image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
cv2.waitKey(10000)
-1
cv2.destroyAllWindows()
image = cv2.resize(image, (800, 600))
image.shape
(600, 800, 3)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
-1
cv2.destroyAllWindows()
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plt.imshow(image_gray)
plt.show()
cv2.waitKey(10000)
cv2.destroyAllWindows()
display (image_gray.shape)
(600, 800)
face_detector = cv2.CascadeClassifier('/content/haarcascade_frontalface_default.xml')
display (face_detector)
detections = face_detector.detectMultiScale(image_gray)
display (detections)
array([[677, 72, 68, 68],
[115, 124, 53, 53],
[475, 123, 59, 59],
[387, 233, 73, 73],
[ 92, 239, 66, 66],
[390, 323, 56, 56]], dtype=int32)
display (len(detections))
6
x=390 # X - Co ordinates
y=323 # Y- Co ordinates
w=56 # Face Width
h=56 # Face Height
cv2.rectangle(image_gray, (x, y), (x + w, y + h), (0,255,255), 1)
plt.imshow(image_gray)
plt.show()
cv2.waitKey(10000)
cv2.destroyAllWindows()
x=115 # X - Co ordinates
y=124 # Y- Co ordinates
w=52 # Face Width
h=52 # Face Height
cv2.rectangle(image_gray, (x, y), (x + w, y + h), (0,255,255), 5)
plt.imshow(image_gray)
plt.show()
cv2.waitKey(10000)
cv2.destroyAllWindows()
x=115 # X - Co ordinates
y=124 # Y- Co ordinates
w=52 # Face Width
h=52 # Face Height
cv2.rectangle(image_gray, (x, y), (x + w, y + h), (0,255,255), 5)
plt.imshow(image_gray)
plt.show()
cv2.waitKey(10000)
cv2.destroyAllWindows()
x=475 # X - Co ordinates
y=123 # Y- Co ordinates
w=59 # Face Width
h=59 # Face Height
cv2.rectangle(image_gray, (x, y), (x + w, y + h), (0,255,255), 5)
plt.imshow(image_gray)
plt.show()
cv2.waitKey(10000)
cv2.destroyAllWindows()
for (x, y, w, h) in detections:
print (x, y, w, h)
677 72 68 68 115 124 53 53 475 123 59 59 387 233 73 73 92 239 66 66 390 323 56 56
cv2.rectangle(image_gray, (x, y), (x + w, y + h), (0,255,255), 5)
plt.imshow(image_gray)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
image = cv2.imread('/content/people1.jpg')
display (image.shape)
image = cv2.resize(image, (800, 600)) # Resize image
display (image.shape)
detections = face_detector.detectMultiScale(image)
display (detections)
(1280, 1920, 3)
(600, 800, 3)
array([[677, 72, 68, 68],
[115, 124, 53, 53],
[475, 123, 59, 59],
[387, 233, 73, 73],
[ 92, 239, 66, 66],
[390, 323, 56, 56]], dtype=int32)
for (x, y, w, h) in detections:
print (x, y, w, h)
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,255), 5)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
677 72 68 68 115 124 53 53 475 123 59 59 387 233 73 73 92 239 66 66 390 323 56 56
image = cv2.imread('/content/people1.jpg')
image = cv2.resize(image, (800, 600)) # Resize image
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = face_detector.detectMultiScale(image_gray, scaleFactor = 1.09)
display (detections)
for (x, y, w, h) in detections:
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 5)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
array([[678, 74, 65, 65],
[113, 122, 56, 56],
[475, 122, 60, 60],
[386, 232, 74, 74],
[ 90, 238, 69, 69]], dtype=int32)
image = cv2.imread('/content/people2.jpg')
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
image = cv2.imread('/content/people2.jpg')
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = face_detector.detectMultiScale(image_gray, scaleFactor = 1.09)
display (detections)
for (x, y, w, h) in detections:
print(w, h)
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
array([[424, 32, 46, 46],
[324, 49, 45, 45],
[663, 83, 43, 43],
[600, 61, 49, 49],
[907, 63, 50, 50],
[718, 101, 44, 44],
[235, 45, 51, 51],
[380, 53, 54, 54],
[ 91, 81, 50, 50],
[168, 35, 50, 50],
[825, 90, 51, 51],
[479, 69, 47, 47],
[692, 153, 44, 44],
[461, 367, 33, 33],
[462, 378, 27, 27]], dtype=int32)
46 46 45 45 43 43 49 49 50 50 44 44 51 51 54 54 50 50 50 50 51 51 47 47 44 44 33 33 27 27
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = face_detector.detectMultiScale(image_gray, scaleFactor = 1.2,minNeighbors=7)
display (detections)
for (x, y, w, h) in detections:
print(w, h)
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
array([[425, 32, 45, 45],
[322, 49, 47, 47],
[716, 102, 44, 44],
[600, 60, 48, 48],
[168, 35, 49, 49],
[662, 83, 42, 42],
[ 93, 82, 48, 48],
[379, 54, 54, 54],
[906, 63, 51, 51],
[824, 91, 52, 52]], dtype=int32)
45 45 47 47 44 44 48 48 49 49 42 42 48 48 54 54 51 51 52 52
image = cv2.imread('/content/people2.jpg')
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = face_detector.detectMultiScale(image_gray, scaleFactor = 1.2,minNeighbors=7)
display (detections)
for (x, y, w, h) in detections:
print(w, h)
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
array([[424, 31, 47, 47],
[321, 48, 47, 47],
[600, 60, 49, 49],
[168, 35, 49, 49],
[380, 54, 52, 52],
[906, 63, 51, 51],
[717, 100, 46, 46],
[662, 83, 44, 44],
[ 91, 81, 51, 51],
[234, 46, 50, 50],
[825, 91, 51, 51],
[480, 70, 47, 47]], dtype=int32)
47 47 47 47 49 49 49 49 52 52 51 51 46 46 44 44 51 51 50 50 51 51 47 47
image = cv2.imread('/content/people2.jpg')
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = face_detector.detectMultiScale(image_gray, scaleFactor = 1.2,
minNeighbors=7,minSize=(20,20), maxSize=(100,100))
display (detections)
for (x, y, w, h) in detections:
print(w, h)
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
array([[424, 31, 47, 47],
[321, 48, 47, 47],
[600, 60, 49, 49],
[168, 35, 49, 49],
[380, 54, 52, 52],
[906, 63, 51, 51],
[717, 100, 46, 46],
[662, 83, 44, 44],
[ 91, 81, 51, 51],
[234, 46, 50, 50],
[825, 91, 51, 51],
[480, 70, 47, 47]], dtype=int32)
47 47 47 47 49 49 49 49 52 52 51 51 46 46 44 44 51 51 50 50 51 51 47 47
eye_detector= cv2.CascadeClassifier('/content/haarcascade_eye.xml')
display (eye_detector)
< cv2.CascadeClassifier 0x7e69554627d0>
image = cv2.imread('/content/people1.jpg')
display (image.shape)
image = cv2.resize(image, (1600,1000)) # Resize image
print(image.shape)
face_detections = face_detector.detectMultiScale(image, scaleFactor = 1.3, minSize = (30,30))
for (x, y, w, h) in face_detections:
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
eye_detections = eye_detector.detectMultiScale(image, scaleFactor = 1.1, minNeighbors=10, maxSize=(60,60))
for (x, y, w, h) in eye_detections:
print(w, h)
cv2.rectangle(image, (x, y), (x + w, y + h), (0,0,255), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
(1280, 1920, 3)
(1000, 1600, 3) 33 33 30 30 23 23 22 22 29 29 33 33 31 31 25 25 22 22
car_detector = cv2.CascadeClassifier('/content/cars.xml')
display (car_detector)
< cv2.CascadeClassifier 0x7e6955304fb0>
image = cv2.imread('/content/car.jpg')
display (image.shape)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
(286, 468, 3)
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = car_detector.detectMultiScale(image_gray, scaleFactor = 1.03, minNeighbors=8)
for (x, y, w, h) in detections:
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
clock_detector = cv2.CascadeClassifier('/content/clocks.xml')
display (clock_detector)
< cv2.CascadeClassifier 0x7e6955376b90>
image = cv2.imread('/content/clock.jpg')
display (image.shape)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
(438, 450, 3)
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = clock_detector.detectMultiScale(image_gray, scaleFactor = 1.03, minNeighbors=1)
for (x, y, w, h) in detections:
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
full_detector = cv2.CascadeClassifier('/content/fullbody.xml')
display (full_detector)
< cv2.CascadeClassifier 0x7e6955374f70>
image = cv2.imread('/content/people3.jpg')
display (image.shape)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
(294, 626, 3)
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detections = full_detector.detectMultiScale(image_gray, scaleFactor = 1.05, minNeighbors=5, minSize = (50,50))
for (x, y, w, h) in detections:
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
Face detection with HOG and Dlib
import dlib
image = cv2.imread('/content/people2.jpg')
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
face_detector_hog = dlib.get_frontal_face_detector()
detections = face_detector_hog(image, 1) # 1 – is the scale factor
print(detections[0])
print(detections[0].left())
print(detections[0].top())
print(detections[0].right())
print(detections[0].bottom())
cv2.rectangle(image, (detections[0].left(), detections[0].top()), (detections[0].right(), detections[0].bottom()), (0, 255, 255), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
[(429, 38) (465, 74)] 429 38 465 74
for face in detections:
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
cv2.rectangle(image, (l, t), (r, b), (0, 255, 255), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
Face detection with CNN and Dlib
image = cv2.imread('/content/people2.jpg')
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
pip install tensorflow
Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.13.0) Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0) Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3) Requirement already satisfied: flatbuffers>=23.1.21 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26) Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0) Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0) Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.59.0) Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.9.0) Requirement already satisfied: keras<2.14,>=2.13.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.13.1) Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6) Requirement already satisfied: numpy<=1.24.3,>=1.22 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.23.5) Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0) Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.2) Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3) Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2) Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0) Requirement already satisfied: tensorboard<2.14,>=2.13 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.13.0) Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.13.0) Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.3.0) Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.5.0) Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.15.0) Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.34.0) Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.2) Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow) (2.17.3) Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow) (1.0.0) Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow) (3.5) Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow) (2.31.0) Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow) (0.7.1) Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.14,>=2.13->tensorflow) (3.0.0) Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow) (5.3.1) Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow) (0.3.0) Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow) (4.9) Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow) (1.3.1) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow) (3.3.0) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow) (3.4) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow) (2.0.7) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow) (2023.7.22) Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow) (2.1.3) Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow) (0.5.0) Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow) (3.2.2)
pip install tensorflow-gpu
import tensorflow as tf
print(tf.__version__)
2.13.0
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Create a sequential model
model = keras.Sequential()
# Add convolutional layers
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
# Flatten the output
model.add(layers.Flatten())
# Add dense layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Summary of the model architecture
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
max_pooling2d (MaxPooling2 (None, 13, 13, 32) 0
D)
conv2d_1 (Conv2D) (None, 11, 11, 64) 18496
max_pooling2d_1 (MaxPoolin (None, 5, 5, 64) 0
g2D)
flatten (Flatten) (None, 1600) 0
dense (Dense) (None, 64) 102464
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 121930 (476.29 KB)
Trainable params: 121930 (476.29 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
import os
os.environ['CUDA_HOME'] = '/usr/local/cuda-X.Y' # Update with your CUDA version
image = cv2.imread('/content/people2.jpg')
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
cnn_detector = dlib.cnn_face_detection_model_v1('/content/mmod_human_face_detector.dat')
display (cnn_detector)
<_dlib_pybind11.cnn_face_detection_model_v1 at 0x7e68d06ea1b0>
detections = cnn_detector(image, 1)
for face in detections:
l, t, r, b, c = face.rect.left(), face.rect.top(), face.rect.right(), face.rect.bottom(), face.confidence
print(c)
cv2.rectangle(image, (l, t), (r, b), (255, 255, 0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
1.1118313074111938 1.1012212038040161 1.0860981941223145 1.0829256772994995 1.072746992111206 1.067101001739502 1.062952995300293 1.0371497869491577 1.0303109884262085 1.0288389921188354 1.0205779075622559 0.8558180332183838 0.8077936172485352
image = cv2.imread('/content/people3.jpg')
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
haarcascade_detector = cv2.CascadeClassifier('/content/haarcascade_frontalface_default.xml')
detections = haarcascade_detector.detectMultiScale(image_gray, scaleFactor = 1.001, minNeighbors=5, minSize = (5,5))
for (x, y, w, h) in detections:
cv2.rectangle(image, (x, y), (x + w, y + h), (0,255,0), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
image = cv2.imread('/content/people3.jpg')
face_detector_hog = dlib.get_frontal_face_detector()
detections = face_detector_hog(image, 4)
for face in detections:
l, t, r, b = (face.left(), face.top(), face.right(), face.bottom())
cv2.rectangle(image, (l, t), (r, b), (0, 255, 255), 2)
plt.imshow(image)
plt.show()
cv2.waitKey(10000)
plt.show()
cv2.destroyAllWindows()
